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Digitizing working group

 
The COVID-19 Evidence Network to support Decision-making (COVID-END) has come together to help coordinate and reduce duplication in these remarkable efforts. Its initial focus includes supporting working groups to achieve and document quick-wins, and to establish processes to achieve and document longer-term wins, in seven areas including digitizing as many aspects of the work as possible to facilitate coordination and capture efficiencies.

Proposed terms of reference

  1. Developing and operationalizing an approach to optimizing and sharing searches, de-duplicated articles, and screen articles (e.g., stable ID for all studies)
  2. Developing a taxonomy of key meta-data that all working groups can use and that leverage work already done by groups like FRBR, MCBK, HL7 (and its health-evidence initiative called EBMonFIHR), and OMG, among others
    1. Topic – capturing everything from diagnosis through managing surge to addressing delays in chronic-disease management (and liaising with the Scoping working group on this part)
    2. Document type - review/study type, derivative product type and target audience focus, etc.
    3. Evidence ‘provenance’
    4. Status
    5. Date – title registration, protocol registration, review target date, search completed date, review completed date
  3. Rationalizing, linking and aggregating metadata across key portals to capture what is being done (as well as for when and how can it be accessed) in ways that follow FAIR data principles (findable, accessible, interoperable, and re-usable)
    1. Questions being asked (e.g., Cochrane question bank, Oxford CEBM questions)
    2. Studies
    3. Evidence syntheses (including those that are relevant to COVID-19 but where the studies were not conducted in the context of COVID-19 (e.g., Evidence Aid))
      1. Registered titles
      2. Registered protocols (e.g., can PROSPERO’s scope be expanded beyond existing topics and review types, can its capacity be expanded to cope with the increased volume, can its data elements be expanded to include anticipated completion date, can follow-up be automated, can preprints be linked, can a results template be used)
      3. Completed reviews, including rapid reviews
      4. Data from completed reviews
    4. Guidelines
    5. Derivative products
  4. Identifying portals that can be strengthened/expanded, joined up or built to fill gaps in any of the above
  5. Identifying, sharing and operationalizing ways to use machine learning to streamline processes
  6. Exploring a potential collaboration with one or more of the COVID-19 Knowledge Accelerator working groups

Participants

  1. Chris Mavergames, Cochrane Collaboration, Germany (co-chair)
  2. Linn Brandt, Magic Evidence Ecosystem Foundation, Norway (co-chair)
  3. Alfonso Iorio, McMaster University, Canada
  4. Brian Alper, EBSCO, United States
  5. Gabriel Rada, Epistemonikos, Chile
  6. Gunn Vist, Norwegian Institute of Public Health, Norway
  7. James Thomas, EPPI Centre, UK
  8. Jerry Osheroff
  9. Jon Brassey, TRIP database, UK
  10. Julian Elliott, Cochrane Australia, Australia
  11. Lesley Stewart, Centre for Research and Dissemination, PROSPERO, (University of York) UK
  12. Tamara Navarro, McMaster University, Canada
  13. Secretariat: Kaelan Moat, and Safa Al-Khateeb, McMaster Health Forum | RISE, Canada, David Tovey, and Jeremy Grimshaw and Anna Dion, Ottawa, Hospital Research Institute | RISE, Canada

Review our guide to all COVID-19 evidence sources to see where we need to digitize as many aspects of the work as possible to facilitate coordination and capture efficiencies.